Mac安装Flume

一：简介

Flume 是Cloudera提供的一个高可用的，高可靠的，分布式的海量日志采集、聚合和传输的系统，Flume支持在日志系统中定制各类数据发送方，用于收集数据；同时，Flume提供对数据进行简单处理，并写到各种数据接受方（可定制）的能力。

Agent主要由:source,channel,sink三个组件组成:

• Source
  从数据发生器接收数据,并将接收的数据以Flume的event格式传递给一个或者多个通道channel,Flume提供多种数据接收的方式,比如Avro,Thrift等
• Channel
  channel是一种短暂的存储容器,它将从source处接收到的event格式的数据缓存起来,直到它们被sinks消费掉,它在source和sink间起着桥梁的作用,channel是一个完整的事务,这一点保证了数据在收发的时候的一致性. 并且它可以和任意数量的source和sink链接. 支持的类型有: JDBC channel , File System channel , Memory channel等.
• sink
  sink将数据存储到集中存储器比如Hbase和HDFS,它从channels消费数据(events)并将其传递给目标地. 目标地可能是另一个sink,也可能HDFS,HBase.

二：安装

1. 安装

brew install flume

2. 配置环境变量

vi ~/.bash_profile

export FLUME_HOME=/usr/local/Cellar/flume/1.9.0_1/libexec
export FLUME_CONF_DIR=$FLUME_HOME/conf
export PATH=$FLUME_HOME/bin:$PATH

source ~/.bash_profile

3. 配置flume-env.sh

cp flume-env.sh.template flume-env.sh

export JAVA_HOME=/Library/Java/JavaVirtualMachines/jdk1.8.0_141.jdk/Contents/Home
export JAVA_OPTS="-Xms100m -Xmx2000m -Dcom.sun.management.jmxremote"

如果不配置JAVA_OPTS会报错Exception in thread “main” java.lang.OutOfMemoryError: Java heap space

4. 查看版本号

cd $FLUME_HOME/conf

flume-ng version

错误: 找不到或无法加载主类 org.apache.flume.tools.GetJavaProperty

解决办法一：可能是机器上安装HBase了，将libexec/conf/hbase-env.sh配置文件中的HBASE_CLASSPATH注释掉。

# export HBASE_CLASSPATH=/usr/local/Cellar/hadoop/3.2.1/libexec/etc/hadoop

解决办法二：HBASE_CLASSPATH改为JAVA_CLASSPATH

export JAVA_CLASSPATH=.:$JAVA_HOME/lib/dt.jar:$JAVA_HOME/lib/tools.jar

5. 启动

1. netcat

libexec/conf/netcat.conf

# Name the components on this agent  
a1.sources = r1
a1.sinks = k1
a1.channels = c1

# Describe/configure the source  
a1.sources.r1.type = netcat
a1.sources.r1.bind = localhost
a1.sources.r1.port = 44444
#同上，记住该端口名

# Describe the sink  
a1.sinks.k1.type = logger

# Use a channel which buffers events in memory  
a1.channels.c1.type = memory
a1.channels.c1.capacity = 1000
a1.channels.c1.transactionCapacity = 100

# Bind the source and sink to the channel  
a1.sources.r1.channels = c1
a1.sinks.k1.channel = c1

sudo flume-ng agent -c . -f netcat.conf -n a1 -Dflume.root.logger=INFO,console

# telnet后输入一些英文(不要输入中文)回车
telnet  localhost 44444

2. Avro

libexec/conf/avro.conf

a1.sources = r1
a1.sinks = k1
a1.channels = c1

# Describe/configure the source
a1.sources.r1.type = avro
a1.sources.r1.channels = c1
a1.sources.r1.bind = 0.0.0.0
a1.sources.r1.port = 4141

#注意这个端口名，在后面的教程中会用得到

# Describe the sink
a1.sinks.k1.type = logger

# Use a channel which buffers events in memory
a1.channels.c1.type = memory
a1.channels.c1.capacity = 1000
a1.channels.c1.transactionCapacity = 100

# Bind the source and sink to the channel
a1.sources.r1.channels = c1
a1.sinks.k1.channel = c1

cd $FLUME_HOME/conf
sudo flume-ng agent -c . -f avro.conf -n a1 -Dflume.root.logger=INFO,console

sudo sh -c 'echo "hello flueme" > /tmp/log.00'

# 读取/tmp/log.00文件中的内容
cd $FLUME_HOME/conf
flume-ng avro-client --conf conf -H localhost -p 4141 -F /tmp/log.00

3. spooldir

libexec/conf/flume.conf 文件需要手动创建。a1.sources.r1.spoolDir对应的目录也需要预先创建好mkdir -p /opt/logs/flume

vim flume.conf

# 指定Agent的组件名称
a1.sources = r1
a1.sinks = k1
a1.channels = c1

# 指定Flume source(要监听的路径)
a1.sources.r1.type = spooldir
a1.sources.r1.spoolDir = /opt/logs/flume
# 指定Flume sink
a1.sinks.k1.type = logger

# 指定Flume channel
a1.channels.c1.type = memory
a1.channels.c1.capacity = 1000
a1.channels.c1.transactionCapacity = 100

# 绑定source和sink到channel上
a1.sources.r1.channels = c1
a1.sinks.k1.channel = c1

sudo flume-ng agent -c . -f flume.conf -n a1 -Dflume.root.logger=INFO,console

sudo sh -c 'echo "hello flume" > /opt/logs/flume/test.log'

三：Avro简介

Avro是Hadoop的一个数据序列化系统, 由Hadoop的创始人Doug Cutting（也是Lucene，Nutch等项目的创始人）开发，设计用于支持大批量数据交换的应用。也作为RPC框架。

1. User.avsc

{
    "namespace": "com.example.avro",
    "type": "record",
    "name": "User",
    "fields": [
    	  {"name": "id", "type": "int"},
        {"name": "name", "type": "string"},
        {"name": "salary", "type": "int"},
        {"name": "age", "type": "int"},
        {"name": "address", "type": "string"}
    ]
}

2. 生成Java类

进入mvnrepository下载对应的Jar

# 根据User.avsc生成.java文件，保存的目录为src
java -jar avro-tools-1.9.2.jar compile schema User.avsc src

3. 引入依赖

<dependency>
    <groupId>org.apache.avro</groupId>
    <artifactId>avro</artifactId>
    <version>1.9.2</version>
</dependency>

4 .将生成的User.java文件放入到项目中

public class AvroTest {
    public static void main(String[] args) throws Exception {
        User user1 = new User();
        user1.setId(1);
        user1.setName("mengday");
        user1.setSalary(1000);
        user1.setAge(20);
        user1.setAddress("beijing");

        User user2 = new User(2, "vbirdbest", 1000, 19, "shanghai");

        User user3 = User.newBuilder()
                .setId(3)
                .setName("bin")
                .setAge(21)
                .setSalary(2000)
                .setAddress("shenzhen")
                .build();

        // 序列化
        String path = "/Users/mengday/Desktop/avro/User.avsc";
        DatumWriter<User> userDatumWriter = new SpecificDatumWriter<User>(User.class);
        DataFileWriter<User> dataFileWriter = new DataFileWriter<User>(userDatumWriter);
        dataFileWriter.create(user1.getSchema(), new File(path));
        dataFileWriter.append(user1);
        dataFileWriter.append(user2);
        dataFileWriter.append(user3);
        dataFileWriter.close();


        // 反序列化
        DatumReader<User> reader = new SpecificDatumReader<User>(User.class);
        DataFileReader<User> dataFileReader = new DataFileReader<User>(new File("/Users/mengday/Desktop/avro/User.avsc"), reader);
        User user = null;
        while (dataFileReader.hasNext()) {
            user = dataFileReader.next();
            System.out.println(user);
        }
    }
}